The present invention relates to a system for feature detection suitable for low contrast images.
There exist many different image matching techniques that are used for image retrieval, object identification, object detection, and object characterization. One such image matching technique uses feature based identification. In particular, the feature based identification may identify corners of one or more images as being indicative of the object, and may also include a set of vectors describing each set of corresponding feature points. The corresponding vectors may be used to compare the similarity of the objects in different images.
In general, even if the captured object in one image is same object as that is captured in another image, the size, the rotation, the brightness and the color of each of the objects are frequently different. Some feature point detection techniques are based upon a Harris Corner Detector, SIFT, and Determinant of Hessian.
The Harris Corner Detector extracts feature points by using a first-order derivative of an image. The SIFT extracts feature points by uses a Difference of Gaussian (DoG), which is similar to a second order derivative, by removing points that have low contrast. The Determinant of Hessian uses a Hessian matrix, which is a second-order derivative of an image. In general, these “derivative based” techniques are able to detect feature points when the contrast remains sufficiently high.
Referring to FIG. 1, another feature point detector further includes histogram based equalization. The histogram based technique expands the global contrast of an image when the original contrast of the image is small. Then the histogram based technique calculates a histogram of code values of the whole image. Then if the difference between the maximum value of histogram and the minimum value of the histogram is sufficiently small, the technique adjusts the histogram by changing the maximum value of histogram to the maximum value of code values and changing the minimum value of histogram to the minimum value of code values. The adjusted image from the histogram based equalization may be further processed by one of the above-described derivative based feature point techniques. Unfortunately, the histogram based equalization technique tends to amplify noise in the image, thereby producing a significant number of false positives.